Analysis of User's Browsing Behavior and Their Categorization Using Markov Chain Model

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Author(s): Ratnesh Kumar Jain (Kendriya Vidyalaya, India)and Rahul Singhai (Devi Ahilya University, India)
Copyright: 2017
Pages: 51
Source title: Web Usage Mining Techniques and Applications Across Industries
Source Author(s)/Editor(s): A.V. Senthil Kumar (Hindusthan College of Arts and Science, India)
DOI: 10.4018/978-1-5225-0613-3.ch002

Keywords: Computer Science & IT / Data Mining and Databases / Information Science Reference / Web Technologies & Engineering

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Abstract

Web server log file contains information about every access to the web pages hosted on a server like when they were requested, the Internet Protocol (IP) address of the request, the error code, the number of bytes sent to the user, and the type of browser used. Web servers can also capture referrer logs, which show the page from which a visitor makes the next request. As the visit to web site is increasing exponentially the web logs are becoming huge data repository which can be mined to extract useful information for decision making. In this chapter, we proposed a Markov chain based method to categorize the users into faithful, Partially Impatient and Completely Impatient user. And further, their browsing behavior is analyzed. We also derived some theorems to study the browsing behavior of each user type and then some numerical illustrations are added to show how their behavior differs as per categorization. At the end we extended this work by approximating the theorems.

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Analysis of User's Browsing Behavior and Their Categorization Using Markov Chain Model

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